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AbstractUnderlying information about failure, including observations made in free text, can be a good source for understanding, analyzing, and extracting meaningful information for determining causation. The unstructured nature of natural language expression demands advanced methodology to identify its underlying features. There is no available solution to utilize unstructured data for risk assessment purposes. Due to the scarcity of relevant data, textual data can be a vital learning source for developing a risk assessment methodology. This work addresses the knowledge gap in extracting relevant features from textual data to develop cause–effect scenarios with minimal manual interpretation. This study applies natural language processing and text‐mining techniques to extract features from past accident reports. The extracted features are transformed into parametric form with the help of fuzzy set theory and utilized in Bayesian networks as prior probabilities for risk assessment. An application of the proposed methodology is shown in microbiologically influenced corrosion‐related incident reports available from the Pipeline and Hazardous Material Safety Administration database. In addition, the trained named entity recognition (NER) model is verified on eight incidents, showing a promising preliminary result for identifying all relevant features from textual data and demonstrating the robustness and applicability of the NER method. The proposed methodology can be used in domain‐specific risk assessment to analyze, predict, and prevent future mishaps, ameliorating overall process safety.

There is a growing body of literature that focuses on the similarities and differences between how people behave in the offline world vs. how they behave in these virtual environments. Data mining has aided in discovering interesting insights with respect to how people behave in these virtual environments. The book addresses prediction, mining and analysis of offline characteristics and behaviors from online data and vice versa. Each chapter will focus on a different aspect of virtual worlds to real world prediction e.g., demographics, personality, location, etc.

What is data-driven HR? -- The evolution of intelligent (and super intelligent) HR -- Data-driven strategy : making a business case for more intelligent HR -- Capitalising on the data explosion : identifying key sources of HR-relevant data -- Data-driven HR tools : turning data into insights with HR analytics -- Potential pitfalls : looking at data privacy, transparency and security -- Data-driven recruitment -- Data-driven employee engagement -- Data-driven employee safety and wellness -- Data-driven learning and development -- Data-driven performance management -- The future of data-driven HR

Review: "Annals of Information Systems comprises serialized volumes that address a specialized topic or a theme, AOIS publishes peer reviewed works in the analytical, technical as well as the organizational side of information systems. The numbered volumes are guest-edited by experts in a specific domain. AOIS volumes are available as individual books as well as a serialized collection."--Jacket

CONSTRUCTING NOMINAL, SCALE OR ORDINAL OUTCOME MEASURESCODING YOUR DATA FOR INPUT INTO SPSS; THE SPARCOTE STUDY; CHAPTER 4 ENTERING DATA INTO SPSS; A FIRST LOOK AT SPSS; SETTING UP THE SPSS DATASET (YOUR RESULTS SPREADSHEET); ENTERING THE DATA INTO SPSS; EDITING THE DATASET; TRANSFERRING THE RESULTS FROM OTHER SOFTWARE INTO SPSS; A FINAL CHECK OF YOUR DATASET; CHAPTER 5 A FIRST LOOK AT THE RESULTS; OPENING AN SPSS DATA FILE; THE IBM SPSS STATISTICS VIEWER; DESCRIPTIVE STATISTICS FOR EACH VARIABLE; THE FREQUENCIES COMMAND; A FIRST DESCRIPTION OF THE SPARCOTE STUDY RESULTS

This book covers an approach to conversational informatics which encompasses science and technology for understanding and augmenting conversation in the network age. A major challenge in engineering is to develop a technology for conveying not just messages but also underlying wisdom. Relevant theories and practices in cognitive linguistics and communication science, as well as techniques developed in computational linguistics and artificial intelligence, are discussed.

Data-driven criminal justice operations have led to the transformation of criminal records into millions of data points. These records are publicly disclosed on the internet, commodified into valuable big data, and leveraged against people. In Digitial Punishment, Sarah Lageson demonstrates the consequences this system has for people, society, and public policy.

"The military produces, senses, and consumes a tremendous amount of data. Developing ways to effectively leverage these troves of data to enhance operations is a persistent goal. This book explores data analytics techniques that military scientists and practitioners around the world are doing to achieve this. The book is organized around data analytics applications in the primary military domains of Personnel, Intelligence, Operations, and Logistics."--Provided by publisher.